1. Field of the Invention
The present invention relates to industrial and societal systems and methods whose performance is affected by a dependency on meteorological data, as well as a stochastic nature of variations in Earth's atmosphere-ocean system. More particularly, the present invention relates to computer-based systems, methods and computer program product that aid in minimizing system performance risks due to meteorological influence for all systems, such as renewable power production facilities, that have a final product or service that is influenced by meteorological variation and meteorological prediction error.
2. Discussion of the Invention
Due to an inability to control wind currents, and a prevailing disbelief that wind speeds can be predicted accurately over any appreciable period of time, electricity from wind power systems is viewed by system operators and power exchanges as being an unreliable source of power, when compared with fossil fuel facilities or hydro facilities, for example. It is most convenient, for planning purposes, to expect power producers to comply with delivery contracts 100% of the time. The predictability of meteorological and oceanographic parameters varies strongly with conditions in these geophysical systems. Consequently, power provided from renewable power production facilities (wind turbine facilities, tidal facilities, solar facilities and the like) are at a disadvantage when it comes to trading power, since there is an inherent uncertainty regarding whether the power will actually be delivered, at a pre-specified time of delivery. Also, the impact of prediction errors on the meteorology dependent activities vary, thus further complicating the reliability of power from renewable sources, especially ones whose production has significant short-term stochastic variability. More generally, examples of activities where a risk follows from prediction uncertainties include the following:                Electrical power production from time-varying wind, solar, wave, ocean currents, and tidal sources that include Production optimization; Maintenance scheduling; Transmission load; Energy trading; Capital investment costs; and Load shedding;        Designation of flight routes for commercial airlines: Jet-stream position; Alternative routes; and Optimal flight path;        Airport operation and issuing of landing permits in severe weather conditions to include: Back-up transportation methods and resources; and Connecting flights problems—chain of reactions; Global courier mail services where choices of delivery method and path are time-critical to include: Fastest route; Alternative methods; and Guarantee of delivery;        Agricultural activities to include: Harvesting crops; Open-air drying of hay; and Fertilizing periods; and        Transportation at sea to include: Avoiding severe weather at sea; Going in to harbor; Docking tankers with oil production platforms; and Towing large structures such as offshore platforms and wind turbine blades.        
As recognized by the present inventors, a limitation with conventional wind power systems is that unless there is some physical media for storing the electrical power (actually energy) at the local generation facility, conventional systems cannot reliably perform in either the balance regulation or the longer term power exchange, e.g., Nord Pool, due to variability of the wind power. Local storage media is expensive, as compared with other generation systems that are able to control the amount of fuel or energy expended to produce a predetermined amount of electrical power. Thus, conventional wisdom in the power industry is that the wind power systems will require substantially more capital to build (on a per/kW output basis) than other systems, in part because the wind power systems are believed to either require a substantial local energy storage facility, or will suffer from being an inherently unreliable source of electric power.
In an article by Söder L., “The Operation Value of Wind Power in the Deregulated Swedish Market”, Royal Institute of Technology, Sweden, Nordic Wind Power Conference Mar. 13-14, 2000, page 5, paragraph 4.1.3, it is explained that for wind power the construction of the exchange makes it difficult to put bids. The bids on Nord Pool have to be put 12 to 36 hours in advance of real delivery. Söder reflects conventional thoughts about the absence of wind power as a fungible asset by stating that this 12 to 36 hour lead time makes it in reality nearly impossible to trade wind power bids since the forecasts normally are too bad for this time. The difficulty of selling wind power because of the unstable nature of the wind is also recognized by Lutz and Weller (pp. 508-511, 1999, European Wind Energy Conference). Thus, while wind power is generally recognized as an environmentally friendly type of power, it is not believed to be as commercially valuable or fungible as other types of electricity such as that generated by fossil fuels.
Hammons, et al. in “Renewable Energy Alternatives for Developed Countries”, IEEE Transactions on Energy Conversion, Vol. 15, No. 4, December 2000, when analyzing the market for ‘green’ energy, states that “[t]he green pool does not aim to meet the instantaneous demand of its customers. Even a portfolio of green generators would be unable to exactly meet the instantaneous power demand of its consumers at every instant, and, as a result, the green pool will have to purchase top-up electricity when demand exceeds generation and conversely sell excess (spill) electricity when generation exceeds demand. Consequently, even if the number of kWh generated over a year equal the demand, there will still be a cost associated with trading of spill and top-up.”
In order to appreciate how a Power Exchange (PX) operates, a short discussion is in order. Nord Pool is described in the present example, although this is just one example of a PX.
Nord Pool Financial Markets
Financial markets offer a trading place for price hedging and risk management. Participants in the power market can use the financial market to hedge sales and purchases for power. Financial electricity contracts and options are instruments for risk management and budgeting of future proceeds and expenses associated with power sales and power purchases.
A financial contract period includes price hedging of a certain amount of power during a fixed time period. Participants who assume a purchase or sale position are guaranteed the agreed-upon price for purchase or sale of the equivalent amount of power on Elspot, which is Nord Pool's market for trade in power contracts for physical delivery. The contract price hedges a fixed amount of power, the same for all hours, during the contract period.
There are two main categories of Eltermin contracts: Futures and Forwards. The contract types differ as to how settlement is carried out during the trading period, i.e. until their due date (settlement week). The same profit and risk profile applies, whether one trades in Futures Contracts or Forward Contracts.
For Futures Contracts, the value of each Participant's contract portfolio is calculated daily, reflecting changes in the market price of the contracts. The daily changes in value are settled financially between the buyer and the seller. Through this process, a portfolio manager can quickly identify and realize losses as well as profits.
For Forward Contracts, there is no cash settlement until the start of the delivery period. Forward settlement “accumulates” daily during the entire trading period and is realized in equal shares every day in the delivery period. Any unrealized profit (positive accumulated forward settlement) for a product series is applied to reduce the security requirement. A participant's daily security requirement consists of minimum security, margin requirement; plus any unrealized losses (negative accumulated forward settlement) minus unrealized profit (positive accumulated forward settlement).
An option is a contract with an asymmetrical risk, which means that different conditions apply for the contracted parties. When combined, options and financial contracts open up for increased possibilities to distribute and manage risk associated with power trade. The possibility to price hedge and limit risk at the same time is improved. Eloptions can be used to “hedge” a power portfolio against a drop or to increase the return of a portfolio. They can also be used to establish a so-called “caps and floor price”. Irrespectively of whether the value of the underlying product increases, drops, or remains constant there are profits to be made.
Trade may be conducted via Nord Pool's electronic trading system or by bidding via telephone (to the help desk function at Nord Pool). Settlement and delivery are carved out as financial price-hedging settlements without any physical delivery of power. Nord Pool has established a system that allows Clearing Customers to trade and clear Financial Power Contracts through Participants who have been authorized as Trading and Clearing Representatives (brokers).
Publications have been presented related to the impact of power production forecast errors in power systems with significant penetration of wind energy. These have been triggered by the aggressive plans of offshore wind energy farm installations in e.g. Denmark, as well as by the impact of wind energy in the power system on the island of Crete.
Nord Pool Eloptions
Options are financial derivative products that are well suited to markets such as the power market, in which there is volatility and price risk—and thus a need for price hedging.
Electric power options, which are traded on the Nordic Power Exchange's Financial Market, are used to manage risk and forecast future income and costs related to trading in electric power contracts. The combined use of electric power options and forward and futures contracts offers greater opportunities for spreading and handling risk associated with power trading.
Electric power options may be used to secure a power portfolio against price declines or increases, or to increase a portfolio's yield.
The electric Power options traded at Nord Pool include European-style Power Options (EPO) and Asian-style Power Options (APO). The power options traded at Nord Pool are standardized, and thus governed by pre-determined option contract specifications. A key difference between EPOs and APOs is that European-style-exercise power options have underlying instruments, whereas Asian-style-exercise power options are settled retroactively against the arithmetic average Elspot system price during a specified period.
Due to market demand, electric Power options were introduced at the Nordic Power Exchange. These options represent an important element in the Power Exchange's expanding product line. The standardization that now applies to the types of options that are most liquid in the Nordic power market was a precondition for the introduction of these options. Trade in power options has been included in the Nord Pool financial market since the autumn of 1999.
Wins Energy Production Prediction Background
The general usefulness of meteorological and statistical forecasts of power production has been established. Thus, Jensen, Pelgrum, and Madsen, (pp 353-356, European Wind Energy Conference 1994) state that “The economic benefit will come from the situation with normal operation conditions where a good wind power prediction with e.g. 12-hour horizon will enable the operators to take into account the wind production on beforehand, instead of regulating the running units as a consequence of an experienced wind production”. In isolated power production and consumption systems planning is typically over a few hours, while utilization of spinning reserves is planned in shorter intervals of say 15 minutes. In larger scale systems connected via transmission grids, planning is typically done on time-scales of the order of days to months and spinning reserve utilization done on one half hour to an hour time-horizon (Kariniotakis et. al., pp 1082-1085, European Wind Energy Conference 1999). By accurate wind power forecasting the additional spinning reserve due to wind power unpredictability can be reduced.
The choice of prediction method depends on the time horizon of the needed forecast. Today generally statistical methods perform better on time horizons out to about 4 hours whereas for longer forecast periods methods utilizing numerical meteorological weather forecast models are superior.
Nielsen and Madsen, (pp 755-758 European Wind Energy Conference 1997) combines statistical models based on auto-regression with meteorological forecasts and states that the meteorological forecast data is of little value below 4 hours forecast. For 3 to 24 hours ahead the reduction, relative to persistence, in standard deviation in prediction error was increasing from 13.7% to 35.6%.
Bossanyi E. A. (Wind Engineering, Vol. 9, No. 1, 1985) shows that Kalman filtering performs best for 1-minute average wind speed where a 10% reduction in rms error relative to persistence was found. Site dependency of the results was noted. Beyer et.al. (pp 349-352, European Wind Energy Conference 1994) applies Neural Network methods to 1 minute and 10 minute forecasts. They found that a 10% improvement in prediction RMS error is easily obtained relative to persistence. Simple schemes were doing just as well as more complex schemes speaking in favor of fast and simple methods.
Evaluations of prediction accuracy for methods based on numerical meteorological models clearly depend on the quality of the underlying meteorological data assimilation and forecasting system. Beyer, Heinemann, Mellinghoff, Monnich, and Waldl, (pp 1070-1073 European Wind Energy Conference 1999) shows that the RMS error is of order 15% for predictions based on a combination of numerical meteorological forecast model and geostrophic drag-law+similarity wind profile using every 6 hour forecasts out to 48 hours prediction. No significant difference between 6 and 24 hours where found. Larger errors, of up to 25-30% RMS, where found for 48 hours predictions. For regional predictions, covering northern Germany, the RMS is below 10% up to 24 hours in advance.
Akylas, Tombrou, Panourgias, and Lalas (pp 329-332 European Wind Energy Conference 1997) found 20-25% improvement relative to persistence prediction for out to 24-hour forecasts at three sites on Crete, with difficult terrain.
Landberg (pp 747-749 European Wind Energy Conference 1997) shows that the DMI-Risoe model system (termed WPPT) predicts power output for a wind energy farm with an error less than 10% for ranges between 1-36 hours. Landberg (pp 1086-1089 European Wind Energy Conference 1999) shows that the mean absolute error of the DMI-Risoe prediction system is 15% of installed capacity (10% for ‘good’ sites and 20% for ‘bad’ sites). Shifting from using a geostrophic drag law to using instead the predicted speed at 10 m height decreases standard deviations in wind error by 20%. Given 20% improvement in wind error standard deviation, wind power standard deviation improvement would be even better.
Watson, Landberg, and Halliday (IEE Proc. Gener. Transm. Distrib., Vol 141, No. 4, July 1994) examined the financial gains from various levels of wind power penetration in the England and Wales national grid. During the simulated fiscal year 1989/90 the load ranged from 14 GW to 47 GW. Total installed power production capacity was of order 70 GW. A wind power capacity ranging from 5 to 40 GW was examined. The difference between a perfect forecast and a meteorological/statistical forecast in terms of fossil fuel savings may be seen as a measure of the cost associated with predictability and risk. For 10 GW this difference was by Watson et. al. found to be of order 30 Million £, increasing to about 200 Million £ at 20 GW and to 700 Million £ at 30 GW, and of the order of 1230 Million £ at 40 GW installed wind power.
The 15% RMS error stated by Landberg (pp 1086-1089 European Wind Energy Conference 1999) corresponds to 150 MW for a 1 GW installation. The market value of a 150 MW prediction error would, e.g. in the Nordic market, correspond to 720.000 NOK/day for a typical Nord Pool price level of about 200 NOK/MWh.
Meibom and Sorensen (pp 375-378 332 European Wind Energy Conference 1999) (Sorensen and Meibom, Renewable Energy 16 (2399) 878-881), concludes that the Danish ENERGY 21 plan (37% wind power penetration in Denmark), with bidding on Nord Pool, results in a 12% average cost of incorrect bidding. The cost following from having to complement the amount actually delivered via the balance market. For a trading price of 200 DKK per MWh and an installed wind energy production capacity of about 20000 GWh per year we get a total cost of incorrect bidding if all wind power is sold on Nord Pool of 480 million DKK a year. According to this scenario a one percent power production prediction improvement thus has a monetary value of about 4.8 million DKK. This may also be seen as a measure of the value of the risk taken by providing a possibly erroneous prediction.
The impact of forecast errors is, as recognized by the present inventions, clearly larger the larger the wind power penetration is in the system. It follows that the risk also increases and as a result the need for insurance for the partners involved in financing, operating and using the power production system. Watson's et. al. analysis is based on a system without hydropower, together with an assumption that nuclear power cannot be turned off at will. As a result, the fraction of the total available wind power that has to be discarded increases with installed capacity of wind power. Since a hydrogenerator has a significantly shorter response time than a nuclear plant, it should be stated that in a hydropower-dominated grid this negative effect would not be present to the same extent. Watson et. al. also analyzes the importance of load shedding on the operations and savings. They find that savings increase with the allowance of loss of load events. They conclude their study by noting that there is a cost penalty related to wind power relative to conventional power sources for wind energy penetrations above roughly 15%.
Present meteorological forecasting techniques gives, according to the above studies, 15-25% more fossil fuel savings than what a simple persistence forecast would allow. In addition to the fossil fuel savings, optimal operation of wind energy production also lead to a decreased need of spinning reserve and consequently lower capital investment costs for such systems. Shedding wind turbines due to grid balance requirements is clearly a waste of both fuel resources and the capital cost associated with the turbines. As explained in co-pending U.S. application Ser. No. 09/749,999, an efficiently operated market where wind energy is of premier quality and grid balancing can be achieved by saving potential energy in hydro power systems is clearly attractive.
Energy management systems with load and production monitoring, prediction and scheduling, such as e.g. CARE developed within the EU Joule III project, are available. However, they do not include financial risk management features, nor any guidance to market actors on whether to supplement predicted production in order to meet contractual obligations.
The Need for Reducing Risk in Energy Systems Via Risk Management (RM)
As previously described, risk stems from several sources in the energy system. Regarding wind power, wind predictability is the largest source of risk. Reducing risk is of interest to several participants, producers, consumers, system operator (TSO) and suppliers of information to the system. To understand the present invention, a basic review of financial risk management is in order. Examples of risk management perspectives found in “The J. P. Morgan/Arthur Andersen Guide to Corporate Risk Management”, 1997, Risk Publications chapter 2, pp 7-12.include:                RM from the investor perspective—Investors seek a risk-adjusted return on capital invested. High risks require a higher return on investments. Reduced risk enables investors to require less return on the same level of investment. Lower risk attracts more investors, therefore a lowered risk through RM is positive for the value of the company.        Cost of financial distress—Cost of financial distress means the risk of a loss leading to a possible bankruptcy. RM is used to minimize the risk of that loss.        Debt capacity and the cost of debt—RM can ensure that debt can be repaid and interest rates can be paid, despite volatile prices of goods purchased or sold.        Investment capacity and the availability of internal capital—Through RM it is possible to finance future investments internally, since operations with lower risk requires less working capital.        Return on risk capital—RM enables companies to use less capital for future periods with uncertain levels of the cash flow. Risk capital is in many cases more expensive than using a RM hedging strategy.        
In identifying risks, there are three major sources of risk in a company:                Transaction exposure which reflects the effects of a price change in quantities bought or sold. Could be the effect of a change in exchange rate or electricity price on future cash flow;        Economic exposure which reflects future losses due to a relative change in the company's competitiveness compared to similar goods. Could be the change in competitiveness if a currency appreciates or if new investments are more efficient than the company's;        Translation exposure which reflects if a company holds assets in a foreign country and a change in exchange rate decreases the local value of the assets.        
Valuation of risk describes how market movements have affected the value of an asset or a contract (i.e., The Portfolio). Comparing the market price of the asset or contract to the purchase or sales price does this. The result is the current profit or loss of the asset or contract if sold today.
Measuring risk is a process of assessing risk in which one tries to look forward to how market movements could affect the values of an asset or a contract in the future. The methods involve trying to estimate the sensitivity in market value of a portfolio to changes in market prices. Often a probability of the result is used. Several methods are used to measure risk:                Sensitivity analysis—the purpose is determine the Portfolio's change in value due to a $ or % change in price of the Portfolio's content. The $ or % change is determined through historical price movements; and        Value at Risk (Risk Metrics)—Value at Risk (VaR) describes the maximum expected loss, with a specified confidence interval, of the Portfolio's value resulting from an adverse price movement that could occur in normal markets over a defined unwind period.        
A common version under statistical assumptions is VaR=Market value of position ×Position volatility×SQRT (unwind period, the period it takes to close the exposure). VaR takes into correlation the degree to which different market prices move in tandem or not. The following formula takes account of correlation's when measuring combined effects of two risks:VaR=√{square root over ([VaR(1)2+VaR(2)2+2(ρ12VaR(1)VaR(2))])}{square root over ([VaR(1)2+VaR(2)2+2(ρ12VaR(1)VaR(2))])}{square root over ([VaR(1)2+VaR(2)2+2(ρ12VaR(1)VaR(2))])}{square root over ([VaR(1)2+VaR(2)2+2(ρ12VaR(1)VaR(2))])}
VaR can also be estimated with a simulation methodology (e.g., Monte Carlo simulation) where probability density functions of future prices are used. One starts with a portfolio including one or several contracts or assets. The possible observations of each portfolio item's price are described by a probability density function (pdf) where correlations are also included. An observation is drawn and the market value of the portfolio is calculated. 20 Multiple observations are drawn and the possible outcome of the portfolio's value can be described by a pdf. 1000 to several 100000 price observations can be used.
Risk willingness is also another factor in risk management. Better data improves risk willingness. When choosing between risk and potential, maximum payoff should be targeted under desired risk level. In FIG. 15, different points on the efficient front are plotted. It shows the expected payoff increasing and risk decreasing when moving from point 1 to point 2, possibly through a risk management decision. Moving from point 2 to point 3 gives higher profit at the same risk level, for any one a desirable movement, also through a risk management decision. To move from point 3 to 4, there is an equal change in payoff and risk, which is desirable for anyone but the risk averse. The risk lover would also appreciate the move from point 4 to 5, because of the increasing payoff, even though there is an even more increasing risk.